| computer science 101 stanford: Programming Methodology Annabelle Mclver, Carroll Morgan, 2002-10-01 Written by the members of the IFIP Working Group 2.3 (Programming Methodology) this text constitutes an exciting reference on the front-line of research activity in programming methodology. The range of subjects reflects the current interests of the members, and will offer insightful and controversial opinions on modern programming methods and practice. The material is arranged in thematic sections, each one introduced by a problem which epitomizes the spirit of that topic. The exemplary problem will encourage vigorous discussion and will form the basis for an introduction/tutorial for its section. |
| computer science 101 stanford: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on the Growth of Computer Science Undergraduate Enrollments, 2018-04-28 The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. |
| computer science 101 stanford: Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008-07-07 Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. |
| computer science 101 stanford: Computer Science Handbook Allen B. Tucker, 2004-06-28 When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chap |
| computer science 101 stanford: Principles of Computer System Design Jerome H. Saltzer, M. Frans Kaashoek, 2009-05-21 Principles of Computer System Design is the first textbook to take a principles-based approach to the computer system design. It identifies, examines, and illustrates fundamental concepts in computer system design that are common across operating systems, networks, database systems, distributed systems, programming languages, software engineering, security, fault tolerance, and architecture.Through carefully analyzed case studies from each of these disciplines, it demonstrates how to apply these concepts to tackle practical system design problems. To support the focus on design, the text identifies and explains abstractions that have proven successful in practice such as remote procedure call, client/service organization, file systems, data integrity, consistency, and authenticated messages. Most computer systems are built using a handful of such abstractions. The text describes how these abstractions are implemented, demonstrates how they are used in different systems, and prepares the reader to apply them in future designs.The book is recommended for junior and senior undergraduate students in Operating Systems, Distributed Systems, Distributed Operating Systems and/or Computer Systems Design courses; and professional computer systems designers. - Concepts of computer system design guided by fundamental principles - Cross-cutting approach that identifies abstractions common to networking, operating systems, transaction systems, distributed systems, architecture, and software engineering - Case studies that make the abstractions real: naming (DNS and the URL); file systems (the UNIX file system); clients and services (NFS); virtualization (virtual machines); scheduling (disk arms); security (TLS) - Numerous pseudocode fragments that provide concrete examples of abstract concepts - Extensive support. The authors and MIT OpenCourseWare provide on-line, free of charge, open educational resources, including additional chapters, course syllabi, board layouts and slides, lecture videos, and an archive of lecture schedules, class assignments, and design projects |
| computer science 101 stanford: Applied Cryptography Bruce Schneier, 2017-05-25 From the world's most renowned security technologist, Bruce Schneier, this 20th Anniversary Edition is the most definitive reference on cryptography ever published and is the seminal work on cryptography. Cryptographic techniques have applications far beyond the obvious uses of encoding and decoding information. For developers who need to know about capabilities, such as digital signatures, that depend on cryptographic techniques, there's no better overview than Applied Cryptography, the definitive book on the subject. Bruce Schneier covers general classes of cryptographic protocols and then specific techniques, detailing the inner workings of real-world cryptographic algorithms including the Data Encryption Standard and RSA public-key cryptosystems. The book includes source-code listings and extensive advice on the practical aspects of cryptography implementation, such as the importance of generating truly random numbers and of keeping keys secure. . . .the best introduction to cryptography I've ever seen. . . .The book the National Security Agency wanted never to be published. . . . -Wired Magazine . . .monumental . . . fascinating . . . comprehensive . . . the definitive work on cryptography for computer programmers . . . -Dr. Dobb's Journal . . .easily ranks as one of the most authoritative in its field. -PC Magazine The book details how programmers and electronic communications professionals can use cryptography-the technique of enciphering and deciphering messages-to maintain the privacy of computer data. It describes dozens of cryptography algorithms, gives practical advice on how to implement them into cryptographic software, and shows how they can be used to solve security problems. The book shows programmers who design computer applications, networks, and storage systems how they can build security into their software and systems. With a new Introduction by the author, this premium edition will be a keepsake for all those committed to computer and cyber security. |
| computer science 101 stanford: Science and Cooking: Physics Meets Food, From Homemade to Haute Cuisine Michael Brenner, Pia Sörensen, David Weitz, 2020-10-20 Based on the popular Harvard University and edX course, Science and Cooking explores the scientific basis of why recipes work. The spectacular culinary creations of modern cuisine are the stuff of countless articles and social media feeds. But to a scientist they are also perfect pedagogical explorations into the basic scientific principles of cooking. In Science and Cooking, Harvard professors Michael Brenner, Pia Sörensen, and David Weitz bring the classroom to your kitchen to teach the physics and chemistry underlying every recipe. Why do we knead bread? What determines the temperature at which we cook a steak, or the amount of time our chocolate chip cookies spend in the oven? Science and Cooking answers these questions and more through hands-on experiments and recipes from renowned chefs such as Christina Tosi, Joanne Chang, and Wylie Dufresne, all beautifully illustrated in full color. With engaging introductions from revolutionary chefs and collaborators Ferran Adria and José Andrés, Science and Cooking will change the way you approach both subjects—in your kitchen and beyond. |
| computer science 101 stanford: Program Arcade Games Paul Craven, 2015-12-31 Learn and use Python and PyGame to design and build cool arcade games. In Program Arcade Games: With Python and PyGame, Second Edition, Dr. Paul Vincent Craven teaches you how to create fun and simple quiz games; integrate and start using graphics; animate graphics; integrate and use game controllers; add sound and bit-mapped graphics; and build grid-based games. After reading and using this book, you'll be able to learn to program and build simple arcade game applications using one of today's most popular programming languages, Python. You can even deploy onto Steam and other Linux-based game systems as well as Android, one of today's most popular mobile and tablet platforms. You'll learn: How to create quiz games How to integrate and start using graphics How to animate graphics How to integrate and use game controllers How to add sound and bit-mapped graphics How to build grid-based games Audience“div>This book assumes no prior programming knowledge. |
| computer science 101 stanford: Classic Computer Science Problems in Java David Kopec, 2020-12-21 Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your new problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz |
| computer science 101 stanford: Concepts in Programming Languages John C. Mitchell, 2003 A comprehensive undergraduate textbook covering both theory and practical design issues, with an emphasis on object-oriented languages. |
| computer science 101 stanford: Introduction to Computation and Programming Using Python, second edition John V. Guttag, 2016-08-12 The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics. |
| computer science 101 stanford: Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers Stephen Boyd, Neal Parikh, Eric Chu, 2011 Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. |
| computer science 101 stanford: Is-703.a Fema, 2010-01-15 Course Overview This course introduces resource management as described in the National Incident Management System (NIMS), and shows how systems for managing resources can be used to improve incident response. The course includes examples of best practices, lessons learned, and job aids to assist the participant in planning for resource management. The course includes the following lessons: -Lesson 1: Resource Management Overview -Lesson 2: Resource Management Planning -Lesson 3: Resource Typing & Readiness -Lesson 4: Resource Management During Incidents -Lesson 5: Resource Management & Complex Incidents -Lesson 6: Course Summary Note: IS-703.a is an updated version of the IS-703 course. The update version of the course includes expanded content about resource typing. If you have successfully completed IS-703, you may want to review the new version of the course, especially lesson 3. For credentialing purposes, the courses are equivalent. Course Objectives: At the conclusion of this course, you should be able to: -Establish systems for describing, inventorying, requesting, and tracking resources. -Activate these systems prior to and during an incident. -Dispatch resources prior to and during an incident. -Deactivate or recall resources during or after incidents. Primary Audience Federal, State, tribal, and local emergency managers; first responders to include incident commanders from all emergency management disciplines; private industry personnel responsible for coordination activities during a disaster; and voluntary agency personnel. Prerequisites N/A. However, completion of IS 700.a, National Incident Management System (NIMS), An Introduction, is recommended. |
| computer science 101 stanford: Structure and Interpretation of Computer Programs Harold Abelson, Gerald Jay Sussman, 2022-05-03 A new version of the classic and widely used text adapted for the JavaScript programming language. Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This new version of the second edition has been adapted for JavaScript. The first three chapters of SICP cover programming concepts that are common to all modern high-level programming languages. Chapters four and five, which used Scheme to formulate language processors for Scheme, required significant revision. Chapter four offers new material, in particular an introduction to the notion of program parsing. The evaluator and compiler in chapter five introduce a subtle stack discipline to support return statements (a prominent feature of statement-oriented languages) without sacrificing tail recursion. The JavaScript programs included in the book run in any implementation of the language that complies with the ECMAScript 2020 specification, using the JavaScript package sicp provided by the MIT Press website. |
| computer science 101 stanford: Introduction to Applied Linear Algebra Stephen Boyd, Lieven Vandenberghe, 2018-06-07 A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples. |
| computer science 101 stanford: Twenty Lectures on Algorithmic Game Theory Tim Roughgarden, 2016-08-30 Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management. |
| computer science 101 stanford: Foundations of Security Christoph Kern, Anita Kesavan, Neil Daswani, 2007-05-11 Software developers need to worry about security as never before. They need clear guidance on safe coding practices, and that’s exactly what this book delivers. The book does not delve deep into theory, or rant about the politics of security. Instead, it clearly and simply lays out the most common threats that programmers need to defend against. It then shows programmers how to make their defense. The book takes a broad focus, ranging over SQL injection, worms and buffer overflows, password security, and more. It sets programmers on the path towards successfully defending against the entire gamut of security threats that they might face. |
| computer science 101 stanford: The Pedagogy of Pathologization Subini Ancy Annamma, 2017-11-15 WINNER OF THE 2019 AESA CRITICS' CHOICE BOOK AWARD WINNER OF THE 2018 NATIONAL WOMEN'S STUDIES ASSOCIATION ALISON PIEPMEIER BOOK PRIZE Linking powerful first-person narratives with structural analysis, The Pedagogy of Pathologization explores the construction of criminal identities in schools via the intersections of race, disability, and gender. amid the prevalence of targeted mass incarceration. Focusing uniquely on the pathologization of female students of color, whose voices are frequently engulfed by labels of deviance and disability, a distinct and underrepresented experience of the school-to-prison pipeline is detailed through original qualitative methods rooted in authentic narratives. The book’s DisCrit framework, grounded in interdisciplinary research, draws on scholarship from critical race theory, disability studies, education, women’s and girl’s studies, legal studies, and more. |
| computer science 101 stanford: Speech & Language Processing Dan Jurafsky, 2000-09 |
| computer science 101 stanford: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| computer science 101 stanford: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| computer science 101 stanford: Reinforcement Learning, second edition Richard S. Sutton, Andrew G. Barto, 2018-11-13 The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. |
| computer science 101 stanford: Computing the Future National Research Council, Computer Science and Telecommunications Board, Committee to Assess the Scope and Direction of Computer Science and Technology, 1992-02-01 Computers are increasingly the enabling devices of the information revolution, and computing is becoming ubiquitous in every corner of society, from manufacturing to telecommunications to pharmaceuticals to entertainment. Even more importantly, the face of computing is changing rapidly, as even traditional rivals such as IBM and Apple Computer begin to cooperate and new modes of computing are developed. Computing the Future presents a timely assessment of academic computer science and engineering (CS&E), examining what should be done to ensure continuing progress in making discoveries that will carry computing into the twenty-first century. Most importantly, it advocates a broader research and educational agenda that builds on the field's impressive accomplishments. The volume outlines a framework of priorities for CS&E, along with detailed recommendations for education, funding, and leadership. A core research agenda is outlined for these areas: processors and multiple-processor systems, data communications and networking, software engineering, information storage and retrieval, reliability, and user interfaces. This highly readable volume examines: Computer science and engineering as a discipline-how computer scientists and engineers are pushing back the frontiers of their field. How CS&E must change to meet the challenges of the future. The influence of strategic investment by federal agencies in CS&E research. Recent structural changes that affect the interaction of academic CS&E and the business environment. Specific examples of interdisciplinary and applications research in four areas: earth sciences and the environment, computational biology, commercial computing, and the long-term goal of a national electronic library. The volume provides a detailed look at undergraduate CS&E education, highlighting the limitations of four-year programs, and discusses the emerging importance of a master's degree in CS&E and the prospects for broadening the scope of the Ph.D. It also includes a brief look at continuing education. |
| computer science 101 stanford: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| computer science 101 stanford: Computer Architecture John L. Hennessy, David A. Patterson, Krste Asanović, 2012 The computing world is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation. This book focuses on the shift, exploring the ways in which software and technology in the 'cloud' are accessed by cell phones, tablets, laptops, and more |
| computer science 101 stanford: The Cambridge Handbook of Computing Education Research Sally A. Fincher, Anthony V. Robins, 2019-02-21 This Handbook describes the extent and shape of computing education research today. Over fifty leading researchers from academia and industry (including Google and Microsoft) have contributed chapters that together define and expand the evidence base. The foundational chapters set the field in context, articulate expertise from key disciplines, and form a practical guide for new researchers. They address what can be learned empirically, methodologically and theoretically from each area. The topic chapters explore issues that are of current interest, why they matter, and what is already known. They include discussion of motivational context, implications for practice, and open questions which might suggest future research. The authors provide an authoritative introduction to the field which is essential reading for policy makers, as well as both new and established researchers. |
| computer science 101 stanford: Creating a Website: The Missing Manual Matthew MacDonald, 2015-06-18 You can easily create a professional-looking website with nothing more than an ordinary computer and some raw ambition. Want to build a blog, sell products, create forums, or promote an event? No problem! This friendly, jargon-free book gives you the techniques, tools, and advice you need to build a site and get it up on the Web. The important stuff you need to know: Master the basics. Learn HTML5, the language of the Web. Design good-looking pages. Use styles to build polished layouts. Get it online. Find a reliable web host and pick a good web address. Use time-saving tools. Learn free tools for creating web pages and tracking your visitors. Attract visitors. Make sure people can find your site through popular search engines like Google. Build a community. Encourage repeat visits with social media. Bring in the cash. Host Google ads, sell Amazon’s wares, or push your own products that people can buy via PayPal. Add pizzazz. Include audio, video, interactive menus, and a pinch of JavaScript. |
| computer science 101 stanford: Introduction to Mathematical Thinking Keith J. Devlin, 2012 Mathematical thinking is not the same as 'doing math'--unless you are a professional mathematician. For most people, 'doing math' means the application of procedures and symbolic manipulations. Mathematical thinking, in contrast, is what the name reflects, a way of thinking about things in the world that humans have developed over three thousand years. It does not have to be about mathematics at all, which means that many people can benefit from learning this powerful way of thinking, not just mathematicians and scientists.--Back cover. |
| computer science 101 stanford: Bitcoin and Cryptocurrency Technologies Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, Steven Goldfeder, 2016-07-19 An authoritative introduction to the exciting new technologies of digital money Bitcoin and Cryptocurrency Technologies provides a comprehensive introduction to the revolutionary yet often misunderstood new technologies of digital currency. Whether you are a student, software developer, tech entrepreneur, or researcher in computer science, this authoritative and self-contained book tells you everything you need to know about the new global money for the Internet age. How do Bitcoin and its block chain actually work? How secure are your bitcoins? How anonymous are their users? Can cryptocurrencies be regulated? These are some of the many questions this book answers. It begins by tracing the history and development of Bitcoin and cryptocurrencies, and then gives the conceptual and practical foundations you need to engineer secure software that interacts with the Bitcoin network as well as to integrate ideas from Bitcoin into your own projects. Topics include decentralization, mining, the politics of Bitcoin, altcoins and the cryptocurrency ecosystem, the future of Bitcoin, and more. An essential introduction to the new technologies of digital currency Covers the history and mechanics of Bitcoin and the block chain, security, decentralization, anonymity, politics and regulation, altcoins, and much more Features an accompanying website that includes instructional videos for each chapter, homework problems, programming assignments, and lecture slides Also suitable for use with the authors' Coursera online course Electronic solutions manual (available only to professors) |
| computer science 101 stanford: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| computer science 101 stanford: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| computer science 101 stanford: Convex Optimization Stephen P. Boyd, Lieven Vandenberghe, 2004-03-08 Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics. |
| computer science 101 stanford: HTML5 Foundations Matt West, 2012-11-19 Master innovative and eye-catching website design with the exciting new Treehouse Series of books Turn plain words and images into stunning websites using HTML5 and this beautiful, full-colour guide. Taking you beyond the constraints of prebuilt themes and simple site building tools, this new Treehouse book combines practicality with inspiration to show you how to create fully customized, modern, and dazzling websites that make viewers want to stop and stay. The exciting new Treehouse Series of books is authored by Treehouse experts and packed with innovative design ideas and practical skill-building. If you're a web developer, web designer, hobbyist, or career-changer, every book in this practical new series should be on your bookshelf. Part of the new Treehouse Series of books, teaching you effective and compelling website development and design, helping you build practical skills Provides career-worthy information from Treehouse industry pros and trainers Explains HTML5 basics, such as how to format text, add scripts to pages, and use HTML5 for audio and video Also covers hypermedia, CSS and JavaScript, embedding video, geolocation, and much more Leverage pages of dazzling website design ideas and expert instruction with a new Treehouse Series book. |
| computer science 101 stanford: Learn to Code HTML and CSS Shay Howe, 2014-04-22 HTML and CSS can be a little daunting at first but fear not. This book, based on Shay Howe's popular workshop covers the basics and breaks down the barrier to entry, showing readers how they can start using HTML and CSS through practical techniques today. They'll find accompanying code examples online, while they explore topics such as the different structures of HTML and CSS, and common terms. After establishing a basic understanding of HTML and CSS a deeper dive is taken into the box model and how to work with floats. The book includes an exercise focused on cleaning up a web page by improving the user interface and design, solely using HTML and CSS. With a few quick changes the web page changes shape and comes to life. Interactive, technically up-to-the-minute and easy-to-understand, this book will advance a student's skills to a professional level. |
| computer science 101 stanford: Introduction to Programming Using Java David Eck, 2009-09 This is a free, on-line textbook on introductory programming using Java. This book is directed mainly towards beginning programmers, although it might also be useful for experienced programmers who want to learn more about Java. It is an introductory text and does not provide complete coverage of the Java language. The text is a PDF and is suitable for printing or on-screen reading. It contains internal links for navigation and external links to source code files, exercise solutions, and other resources. Contents: 1) Overview: The Mental Landscape. 2) Programming in the Small I: Names and Things. 3) Programming in the Small II: Control. 4) Programming in the Large I: Subroutines. 5) Programming in the Large II: Objects and Classes. 6) Introduction to GUI Programming. 7) Arrays. 8) Correctness and Robustness. 9) Linked Data Structures and Recursion. 10) Generic Programming and Collection Classes. 11) Files and Networking. 12) Advanced GUI Programming. Appendices: Source Code for All Examples in this Book, and News and Errata. |
| computer science 101 stanford: The IVY System , 1961 |
| computer science 101 stanford: Text Data Management and Analysis ChengXiang Zhai, Sean Massung, 2016-06-30 Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data. |
| computer science 101 stanford: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| computer science 101 stanford: Introduction to Computing David Evans, 2011-12-07 Introduction to Computing is a comprehensive text designed for the CS0 (Intro to CS) course at the college level. It may also be used as a primary text for the Advanced Placement Computer Science course at the high school level. |
| computer science 101 stanford: Essential C++ Stanley B. Lippman, 2000 Finally, a great introduction to ANCI C++ for working programmers! Lippmann--who worked under the leadership of Bjarne Stroustrup, wrote the classic C++ Primer, and now works as a C++ programmer at DreamWorks--teaches programmers exactly what they need to know to get immediate results. From start to finish, each concept and technique is presented through real programs designed to solve the problems C++ programmers are most likely to encounter. |
Computer - Wikipedia
A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) …
Micro Center - Computer & Electronics Retailer - Shop Now
Shop Micro Center for electronics, PCs, laptops, Apple products, and much more. Enjoy in-store pickup, top deals, and expert same-day tech support.
What is a Computer? - GeeksforGeeks
Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
Laptop & Desktop Computers - Staples
Buy the computer that fits your exact needs. Choose from laptops, desktops PCs, notebooks, and accessories. Invest in a quality computer for work or personal use.
What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
CS 107 Reader - Stanford University
Systems, which serves as the first systems course in the Stanford Computer Science Curriculum. The guide is not intended to replace the outstanding course textbooks, Computer …
CS140 – Operating Systems - Stanford University
Administrivia 2 • Staff mailing list: cs140-staff@scs.stanford.edu - Please mail staff list rather than individuals for help • Google group 12au-cs140 is main discussion forum • Key dates: - …
Cs 101 Stanford
Cs 101 Stanford Course Technology Stanford Computer Science Department Research Report Stanford University. Computer Science Department,R. Davis,M. Wright,1974 Bibliography of …
Objects and Memory - Computer Science
who shaped computer science in its early years. In his master’s thesis, Shannon showed how it was possible to use Boolean logic and switching circuits to perform arithmetic calculations. …
CS107 Lecture 3 - web.stanford.edu
2 Overflow If you exceed the maximum value of your bit representation, you wrap around, or overflow, to the smallest bit representation. 0b1111 + 0b1 = 0b0000 0b1111 + 0b10 = 0b0001 …
Lecture 01: Welcome to CS110: P rinciples of Computer …
Not stocked at Stanford Bookstore b y design. You can buy a copy of it from Amazon if y ou want. "Computer Systems: A Programmer's Perspective", by Bryant and O'Hallaron "Principles of …
This is CS 50. Harvard College’s Introduction to Computer …
z C. Source code. Compilers. Object code. SSH. SFTP. GCC. Functions. Comments. Standard output. Arithmetic operators. Precedence. Associativity.
Data Science 101 Stanford (2024) - archive.ncarb.org
He has also worked at Google and several Big Data startups He has a BS in physics and math from Stanford University and an MS in computer science from Carnegie Mellon Data Science …
Suggested Reading 1. Introduction to Computers, Peter …
Knowing computer: Basic Applications of Computer; Components of Computer System, Central Processing Unit (CPU), VDU, Keyboard and Mouse, Other input/output Devices, Computer …
INTRODUCTION TO INFORMATION THEORY - Stanford …
2 INTRODUCTION TO INFORMATION THEORY P(X∈ A) = Z x∈A dpX(x) = Z I(x∈ A) dpX(x) , (1.3) where the second form uses the indicator function I(s) of a logical statement
L20N Software Engineering - Stanford University
- Computer science classes, particularly introductory CS classes, generally focus on how to program. - However, programming is only part of the much wider set of activities that are …
Lecture #4-5: Computer Hardware (Overview and CPUs)
Instruction Set Computing (CISC). Stanford President John Hennessy won the Turing Award ( omputer Science’s equivalent of the Nobel Prize) for his work on RIS computing. Hardware …
B. Sc computer Science I and II Semester Syllabus
BACHELOR OF SCIENCE IN COMPUTER SCIENCE SYLLABUS FOR UNDER GRADUATE (I and Il Semester) STATE EDUCATION POLICY-2024 WITH EFFECT FROM THE ACADEMIC …
RESUMES/COVER LETTERS - Stanford Career Education
• Course work includes economics, organizational behavior, computer science, and statistics • GPA - 3.8/4.0 6/XX-9/XX Oxford University, Stanford-in-Oxford, Oxford, England • Studied …
Introduction to Multi-Agent Systems - Stanford University
other applications of mechanism design in computer science. 7.2 A Formal Model And Some General Results Before we begin to answer the questions posed in the preceding examples, …
Tarski’s influence on computer science - Stanford University
those big shiny Oracle towers on Highway 101? They would never have been built without Tarski’s work on the recursive definitions of satisfaction and truth.” It took me a while to see in …
Large-scale Video Classification with Convolutional Neural …
UCF-101 by re-purposing low-level features learned on the Sports-1M dataset than by training the entire network on UCF-101 alone. Furthermore, since only some classes in UCF-101 are …
Data Science 101 Stanford [PDF] - archive.ncarb.org
Data Science 101 Stanford: ... Google and several Big Data startups He has a BS in physics and math from Stanford University and an MS in computer science from Carnegie Mellon Data …
F i n a l E x a m - Stanford University
2 of 1 9 1 . C ou rse Th e m e s a ) ( 6 p oi n ts) N a me tw o d i f f er en t ex a mp l es of sh a r ed r esou r ces i n comp u ter sci en ce.
Lecture #4: Pixels and Filters - Stanford University
Department of Computer Science Stanford University Stanford, CA 94305 {bhicks2, arshava, rodion1, cxphan, jameso2}@cs.stanford.edu Contents: 1.Image Sampling and Quantization …
Stanford WON SEOB SEO STATEMENT OF …
computer science 101 please note: some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. this statement does not …
CS105: Introduction to Computer Science - Stanford University
CS105: Introduction to Computer Science Dr. Patrick Young Winter 2010 Class Objectives The course objectives are as follows: to give students a strong background in the fundamentals of …
NATURAL LANGUAGE INFERENCE A DISSERTATION
Abstract Inference has been a central topic in artificial intelligence from the start, but while automaticmethodsforformaldeductionhaveadvancedtremendously,comparatively
RecipeNet: Image to Recipe/Nutritional Information Generator
Computer Science Stanford University dorianrm@stanford.edu Sabrina Lu Mathematical & Computational Science Stanford University slu12@stanford.edu Luciano Gonzalez ... holistic …
Stanford Online Computer Science 101 - crm.hilltimes.com
Stanford Online Computer Science 101: Applied Cryptography Bruce Schneier,2017-05-25 From the world s most renowned security technologist Bruce Schneier this 20th Anniversary Edition …
TABLE OF CONTENTS v Table of Contents - Stanford University
Computer Science: The Mechanization of Abstraction 1 1.1. What This Book Is About 3 1.2. What This Chapter Is About 6 1.3. Data Models 6 1.4. The C Data Model 13 ... Simplifying Big-Oh …
UIC Undergraduate Catalog 2024-2025
Undergraduate Catalog
Lecture Notes on Computational Complexity - Stanford …
Lecture Notes on Computational Complexity Luca Trevisan1 Notes written in Fall 2002, Revised May 2004 1Computer Science Division, U.C. Berkeley.Work supported by NSF Grant No. …
CRYPTANALYSIS OF RSA USING ALGEBRAIC AND LATTICE …
submitted to the department of computer science and the committee on graduate studies of stanford university in partial fulfillment of the requirements for the degree of doctor of …
Data Science 101 Stanford (2024) - archive.ncarb.org
He has also worked at Google and several Big Data startups He has a BS in physics and math from Stanford University and an MS in computer science from Carnegie Mellon Data Science …
Introduction To Computer Science [PDF] - wclc2017.iaslc.org
Computer Science 101 - Stanford Online CS101 is a self-paced course that teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very …
Judith Ellen Fan - cap.stanford.edu
Assistant Professor of Psychology, by courtesy, of Education and of Computer Science Bio BIO I direct the Cognitive Tools Lab (https://cogtoolslab.github.io/) at Stanford University. Our lab …
Data Science 101 Stanford (book) - archive.ncarb.org
Stanford University and an MS in computer science from Carnegie Mellon Data Science in Context Alfred Z. Spector,Peter Norvig,Chris Wiggins,Jeannette M. Wing,2022-10-20 Four …
Probability for Computer Scientists - Stanford University
2020-07-2920:12:29-07:00,draft:sendcommentstomossr@cs.stanford.edu toc 1.3.theinclusion-exclusionprinciple 3 Problem: Considerahashtablewith100 buckets.Twoarbitrarystringsare
Data Science 101 Stanford (PDF) - archive.ncarb.org
Data Science 101 Stanford Jure Leskovec,Jurij Leskovec,Anand ... physics and math from Stanford University and an MS in computer science from Carnegie Mellon Data Science in …
INTRODUCTIONTO THEORETICAL COMPUTERSCIENCE
6 III Eicientalgorithms 385 12 Eicientcomputation: Aninformalintroduction 387 13 Modelingrunningtime 407 14 Polynomial-timereductions 439 15 …
q 0 q sta CS103 rt 0 0, 1 - Stanford University
3.3 Induction and Recursion 101 Monoids and Folds ★ 106 3.4 Variants on Induction 110 Starting Induction Later 110 ... Computer science is the art of solving problems with computers. This is …
DATA ANALYTICS: INTEGRATION AND PRIVACY A …
Ullman for being the perfect computer scientist who taught us how exactly we should be doing theoretical and practical computer science. It was an honor to be working in an office right …
Adversarial Approaches to Debiasing Word Embeddings
Department of Computer Science Department of Computer Science gsychi@stanford.edu bencliu@stanford.edu Abstract In recent years, word embeddings have been ever more …
0 sta CS103 rt 0 0, 1 - Stanford University
Computer science is the art of solving problems with computers. This is a broad definition that encompasses an equally broad field. Within computer science, we find software engineering, …
Panoramas - Stanford University
Computer Science Department Stanford University CS 178, Spring 2011 Panoramas! Marc Levoy ... 101 Ranch, Oklahoma, circa 1920! Marc Levoy Panoramic cameras 6 ... Introduction to …
CS161: Data Structures and Algorithms Handout - Stanford …
Stanford University Tuesday, 16 April 2002 The following is a copy of the Theoretical Computer Science Cheat Sheet by Seiden, from the December 1996 issue of ACM Sigact News. ... 101 …
Stanford Data Science Director’s Report
Data Science Stanford Data Science Director’s Report Fall 2022–Fall 2023 ... and Brian Hie , BS/MS ’16 (Computer Science), jointly appointed in the Department of Chemical Engineering …
Artificial Intelligence Definitions
emeritus Stanford Professor John McCarthy in 1955, was defined by him as “the science and engineering of making intelligent machines”. Much research has humans ... ML draws from …
Deconstructing Disengagement: Analyzing Learner …
Dept. of Computer Science Stanford University Stanford, CA-94305 piech@cs.stanford.edu Emily Schneider School of Education Stanford University Stanford, CA-94305 elfs@cs.stanford.edu …
TABLE OF CONTENTS v Table of Contents - gatech.edu
This book developed from notes for a two-quarter course at Stanford — called CS109: Introduction to Computer Science — that serves a number of goals. The first goal is to give …
CS143 Notes: Parsing - Stanford University
computer science at its best. We will cover only the most widely used and useful algorithms, though. You should be aware that there is a huge body of literature on di erent algorithms and …
INTRODUCTION MACHINE LEARNING - robotics.stanford.edu
Department of Computer Science Stanford University Stanford, CA 94305 e-mail: nilsson@cs.stanford.edu ... 7.6 Relationships Between ILP and Decision Tree Induction . . . . . …
COMPUTER SCIENCE - Stanford University
In addition to Computer Science itself, Stanford offers several inter-disciplinary degrees with a substantial computer science component. The Computer Systems Engineering major (also in …
Food Image Classification with Convolutional Neural Networks
Department of Computer Science Stanford University malinaj@stanford.edu Abstract Food images dominate across social media platforms and drive restaurant selection and travel, but …
